Benchmarks Explained

LLM benchmarks are standardized tests or tasks designed to evaluate the performance of large language models across various capabilities. These benchmarks help researchers and developers compare different models, track progress in the field, and identify areas for improvement. These benchmarks provide valuable insights into the strengths and weaknesses of different LLMs. However, it's important to note that performance on these benchmarks doesn't necessarily translate directly to real-world performance, and the field is constantly evolving with new and more challenging benchmarks being developed.

Important LLM benchmarks:

TruthfulQA: Focuses on evaluating a model's ability to provide truthful answers and avoid generating false or misleading information.

IFEval (Instruction Following Evaluation): Testing capabilities of an LLM to complete various instruction-following tasks.

BBH (Big Bench Hard): A subset of tasks from the BIG-bench benchmark chosen because LLMs usually fail to complete them.

BIG-bench: A collaborative effort to evaluate and predict the capabilities of large language models through over 200 diverse tasks.

GSM8K: Focuses on grade-school level mathematics problems.

GPQA (Google-Proof Questions and Answers): A dataset of extremely challenging graduate-level questions in biology, physics, and chemistry. Designed to test the ability of AI and human experts, even with internet access, to answer without domain-specific knowledge.

MuSR (Multi-step Soft Reasoning): Tests LLMs ability to extract facts, apply commonsense, and perform multi-step reasoning, highlighting gaps in current models reasoning abilities.

MMLU (Massive Multitask Language Understanding): A comprehensive test covering subjects across STEM, humanities, and social sciences, designed to assess models' general knowledge and reasoning abilities.

MMLU-PRO (Massive Multitask Language Understanding - Professional): An enhanced version of the MMLU designed to be more robust and challenging.

GLUE (General Language Understanding Evaluation): A collection of nine tasks testing natural language understanding, including sentiment analysis, textual entailment, and question answering.

SuperGLUE: A more challenging successor to GLUE, featuring more difficult tasks that require more nuanced language understanding.

HELM (Holistic Evaluation of Language Models): A framework for comprehensive evaluation of language models across multiple dimensions, including accuracy, robustness, fairness, and efficiency.

WinoGrande: Designed for testing common sense reasoning by choosing the correct option to "fill in the blank".

SQuAD (Stanford Question Answering Dataset): A reading comprehension dataset consisting of questions posed on a set of Wikipedia articles.

LAMBADA (LAnguage Modeling Broadened to Account for Discourse Aspects): Tests the model's ability to understand long-range dependencies in text by predicting the last word of a given passage.

HumanEval: A coding benchmark that evaluates a model's ability to generate functionally correct Python code based on docstrings which mush then pass included tests.